Text Generation
Transformers
English
qwen2
code-generation
python
fine-tuning
Qwen
tools
agent-framework
multi-agent
conversational
Eval Results (legacy)
Instructions to use my-ai-stack/Stack-2-9-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use my-ai-stack/Stack-2-9-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="my-ai-stack/Stack-2-9-finetuned") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("my-ai-stack/Stack-2-9-finetuned") model = AutoModelForCausalLM.from_pretrained("my-ai-stack/Stack-2-9-finetuned") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use my-ai-stack/Stack-2-9-finetuned with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "my-ai-stack/Stack-2-9-finetuned" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "my-ai-stack/Stack-2-9-finetuned", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/my-ai-stack/Stack-2-9-finetuned
- SGLang
How to use my-ai-stack/Stack-2-9-finetuned with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "my-ai-stack/Stack-2-9-finetuned" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "my-ai-stack/Stack-2-9-finetuned", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "my-ai-stack/Stack-2-9-finetuned" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "my-ai-stack/Stack-2-9-finetuned", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use my-ai-stack/Stack-2-9-finetuned with Docker Model Runner:
docker model run hf.co/my-ai-stack/Stack-2-9-finetuned
| // Simple logger utility for Stack 2.9 | |
| type LogLevel = 'debug' | 'info' | 'warn' | 'error' | |
| const LOG_LEVELS: Record<LogLevel, number> = { | |
| debug: 0, | |
| info: 1, | |
| warn: 2, | |
| error: 3, | |
| } | |
| let currentLevel: LogLevel = 'info' | |
| export function setLogLevel(level: LogLevel): void { | |
| currentLevel = level | |
| } | |
| function shouldLog(level: LogLevel): boolean { | |
| return LOG_LEVELS[level] >= LOG_LEVELS[currentLevel] | |
| } | |
| function formatMessage(level: LogLevel, message: string, data?: unknown): string { | |
| const timestamp = new Date().toISOString() | |
| const dataStr = data ? ` ${JSON.stringify(data)}` : '' | |
| return `[${timestamp}] [${level.toUpperCase()}] ${message}${dataStr}` | |
| } | |
| export function debug(message: string, data?: unknown): void { | |
| if (shouldLog('debug')) { | |
| console.log(formatMessage('debug', message, data)) | |
| } | |
| } | |
| export function log(message: string, data?: unknown): void { | |
| if (shouldLog('info')) { | |
| console.log(formatMessage('info', message, data)) | |
| } | |
| } | |
| export function warn(message: string, data?: unknown): void { | |
| if (shouldLog('warn')) { | |
| console.warn(formatMessage('warn', message, data)) | |
| } | |
| } | |
| export function error(message: string, data?: unknown): void { | |
| if (shouldLog('error')) { | |
| console.error(formatMessage('error', message, data)) | |
| } | |
| } | |
| export default { debug, log, warn, error, setLogLevel } |